Previous [ 1] [ 2] [ 3] [ 4] [ 5] [ 6] [ 7] [ 8] [ 9] [ 10] [ 11] [ 12] [ 13] [ 14] [ 15] [ 16] [ 17] [ 18] [ 19] [ 20] [ 21]

@

Journal of Information Science and Engineering, Vol. 30 No. 3, pp. 749-763 (May 2014)


Adaptive Search Range and Multi-Mutation Strategies for Differential Evolution*


SHENG TA-HSIEH1, SHIH-YUAN CHIU2 AND SHI-JIM YEN2
1Department of Communication Engineering
Oriental Institute of Technology
New Taipei City, 220 Taiwan
2Department of Computer Science and Information Engineering
National Dong Hwa University
Hualien, 974 Taiwan
E-mail: fo013@mail.oit.edu.tw

In this paper, an improved DE is proposed to improve optimization performance by involving four searching strategies: current-to-better mutation, real-random-mutation, sharing mutation, and focused search. When evolution speed is standstill, sharing mutation can increase the search depth; in addition, real-random mutation can disturb individuals and can help individuals diverge to local optimum, focused search can do largescale searches around the best particle. When the evolution progresses well, current-tobetter mutation will drive individuals to the correct evolution direction. Experiments were conducted on all of CEC 2005 test functions, include unimodal, multimodal and hybrid composition functions, to present performance of the proposed method and to compare with 5 variants of DE includes JADE, jDE, SaDE, DEGL and MDE_pBX. The proposed method exhibits better performance than other five related works in solving most the test functions.

Keywords: differential evolution, sharing mutation, optimization, real random mutation, focused search

Full Text () Retrieve PDF document (201405_13.pdf)

Received February 28, 2013; accepted June 15, 2013.
Communicated by Hung-Yu Kao, Tzung-Pei Hong, Takahira Yamaguchi, Yau-Hwang Kuo, and Vincent Shin- Mu Tseng.
* This work was supported in part by National Science Council of Taiwan, Taiwan, under Grant NSC 101-2221- E-161-011.